multiclass_classification_objective¶

Module Contents¶

Classes Summary¶

MulticlassClassificationObjective

Base class for all multiclass classification objectives.

Contents¶

class evalml.objectives.multiclass_classification_objective.MulticlassClassificationObjective[source]¶

Base class for all multiclass classification objectives.

Attributes

problem_types

[ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_MULTICLASS]

Methods

`calculate_percent_difference`	Calculate the percent difference between scores.
`greater_is_better`	Returns a boolean determining if a greater score indicates better model performance.
`is_bounded_like_percentage`	Returns whether this objective is bounded between 0 and 1, inclusive.
`is_defined_for_problem_type`
`name`	Returns a name describing the objective.
`objective_function`	Computes the relative value of the provided predictions compared to the actual labels, according a specified metric
`perfect_score`	Returns the score obtained by evaluating this objective on a perfect model.
`positive_only`	If True, this objective is only valid for positive data. Default False.
`score`	Returns a numerical score indicating performance based on the differences between the predicted and actual values.
`score_needs_proba`	Returns a boolean determining if the score() method needs probability estimates. This should be true for objectives which work with predicted probabilities, like log loss or AUC, and false for objectives which compare predicted class labels to the actual labels, like F1 or correlation.
`validate_inputs`	Validates the input based on a few simple checks.

classmethod calculate_percent_difference(cls, score, baseline_score)¶

Calculate the percent difference between scores.

Parameters

score (float) – A score. Output of the score method of this objective.
baseline_score (float) – A score. Output of the score method of this objective. In practice, this is the score achieved on this objective with a baseline estimator.

Returns

The percent difference between the scores. Note that for objectives that can be interpreted: as percentages, this will be the difference between the reference score and score. For all other objectives, the difference will be normalized by the reference score.

Return type

float

property greater_is_better(cls)¶: Returns a boolean determining if a greater score indicates better model performance.

property is_bounded_like_percentage(cls)¶: Returns whether this objective is bounded between 0 and 1, inclusive.

classmethod is_defined_for_problem_type(cls, problem_type)¶

property name(cls)¶: Returns a name describing the objective.

abstract classmethod objective_function(cls, y_true, y_predicted, X=None, sample_weight=None)¶

Computes the relative value of the provided predictions compared to the actual labels, according a specified metric

Arguments:
y_predicted (pd.Series): Predicted values of length [n_samples] y_true (pd.Series): Actual class labels of length [n_samples] X (pd.DataFrame or np.ndarray): Extra data of shape [n_samples, n_features] necessary to calculate score sample_weight (pd.DataFrame or np.ndarray): Sample weights used in computing objective value result

Returns: Numerical value used to calculate score

property perfect_score(cls)¶: Returns the score obtained by evaluating this objective on a perfect model.

positive_only(cls)¶: If True, this objective is only valid for positive data. Default False.

score(self, y_true, y_predicted, X=None, sample_weight=None)¶

Returns a numerical score indicating performance based on the differences between the predicted and actual values.

Parameters

y_predicted (pd.Series) – Predicted values of length [n_samples]
y_true (pd.Series) – Actual class labels of length [n_samples]
X (pd.DataFrame or np.ndarray) – Extra data of shape [n_samples, n_features] necessary to calculate score
sample_weight (pd.DataFrame or np.ndarray) – Sample weights used in computing objective value result

Returns

score

property score_needs_proba(cls)¶: Returns a boolean determining if the score() method needs probability estimates. This should be true for objectives which work with predicted probabilities, like log loss or AUC, and false for objectives which compare predicted class labels to the actual labels, like F1 or correlation.

validate_inputs(self, y_true, y_predicted)¶

Validates the input based on a few simple checks.

Parameters

y_predicted (pd.Series, or pd.DataFrame) – Predicted values of length [n_samples]
y_true (pd.Series) – Actual class labels of length [n_samples]

Returns

None

lead_scoring objective_base